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Estimating the Fraction of Severe Malaria among Malaria-Positive Children: Analysis of Household Surveys in 19 Malaria-Endemic Countries in Africa.

ABSTRACT: To date, the only robust estimates of severe malaria cases include children who present to the formal healthcare system. It is a challenge to use these data because of varying age ranges of reporting, different diagnosis techniques, surveillance methods, and healthcare utilization. This analysis examined data from 37 Demographic and Health Surveys and Malaria Indicator Surveys across 19 countries in sub-Saharan Africa collected between 2011 and 2018. The outcome of interest is a proxy indicator for severe malaria, defined as a proportion of children aged 6-59 months with at least one self-reported symptom of severe illness including loss of consciousness, rapid breathing, seizures, or severe anemia (hemoglobin < 5 g/dL) among those who were positive for malaria. The study includes a weighted descriptive, country-level analysis and a multilevel mixed-effects logistic regression model to assess the determinants of severe malaria. Among children positive for malaria across all surveys, 4.5% (95% CI: 4.1-4.8) had at least one sign or symptom of severe malaria, which was significantly associated with age, residence, wealth, and year of survey fieldwork at a P-value less than 0.05. This analysis presents a novel and an alternative approach of estimating the fraction of severe malaria cases among malaria-positive children younger than 5 years in malaria-endemic countries. Estimating severe malaria cases through population-based surveys allows countries to estimate severe malaria across time and to compare with other countries. Having a population-level estimate of severe malaria cases helps further our understanding of the burden and epidemiology of severe malaria.

SUBMITTER: Taylor C

PROVIDER: S-EPMC8045660 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Malaria is one of the major causes of childhood death in sub-Saharan countries. A reliable estimation of malaria prevalence is important to guide and monitor progress toward control and elimination. The aim of the study was to estimate the true prevalence of malaria in children under five in the Democratic Republic of the Congo, Uganda and Kenya, using a Bayesian modelling framework that combined in a novel way malaria data from national household surveys with external information about the sensitivity and specificity of the malaria diagnostic methods used in those surveys-i.e., rapid diagnostic tests and light microscopy. METHODS:Data were used from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS) conducted in the Democratic Republic of the Congo (DHS 2013-2014), Uganda (MIS 2014-2015) and Kenya (MIS 2015), where information on infection status using rapid diagnostic tests and/or light microscopy was available for 13,573 children. True prevalence was estimated using a Bayesian model that accounted for the conditional dependence between the two diagnostic methods, and the uncertainty of their sensitivities and specificities obtained from expert opinion. RESULTS:The estimated true malaria prevalence was 20% (95% uncertainty interval [UI] 17%-23%) in the Democratic Republic of the Congo, 22% (95% UI 9-32%) in Uganda and 1% (95% UI 0-3%) in Kenya. According to the model estimations, rapid diagnostic tests had a satisfactory sensitivity and specificity, and light microscopy had a variable sensitivity, but a satisfactory specificity. Adding reported history of fever in the previous 14 days as a third diagnostic method to the model did not affect model estimates, highlighting the poor performance of this indicator as a malaria diagnostic. CONCLUSIONS:In the absence of a gold standard test, Bayesian models can assist in the optimal estimation of the malaria burden, using individual results from several tests and expert opinion about the performance of those tests.

Dataset Information

Estimating the Fraction of Severe Malaria among Malaria-Positive Children: Analysis of Household Surveys in 19 Malaria-Endemic Countries in Africa.

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